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[K Engine] Intorduction To Machine Planning, A Case Based Approach

Date post: 24-May-2015
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My presentation to KEngine company about machine planning
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Introduction to Machine Planning Case Based Approach
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Page 1: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Introduction to Machine Planning

Case Based Approach

Page 2: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Agenda

Case-Based Reasoning (CBR) Case-Based Planning (CBP)

Page 3: [K Engine] Intorduction To Machine Planning, A Case Based Approach

What is Case-Based Reasoning?

CBR is a problem solving paradigm that instead of relying on general knowledge about the problem domain, CBR is able to utilize cases.

Page 4: [K Engine] Intorduction To Machine Planning, A Case Based Approach

What is Case-Based Reasoning?

CBR is an approach to incremental learning, since new experience is retained each time a problem has been solved, making it available for future problems.

Page 5: [K Engine] Intorduction To Machine Planning, A Case Based Approach

What is a Case?

Case is a specific knowledge of

previously experienced concrete problem situation which has been captured and learned in a way that it can be reused in solving future problems.

Page 6: [K Engine] Intorduction To Machine Planning, A Case Based Approach

What is a Case?

New problem to be solved is called new case, or unsolved case.

Solved CaseUnsolved

CaseSolv

e

Previous experience is known as retained case, solved case, past case.

Page 7: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Case-based Problem Solving

Reasoning by re-using past cases is powerful and frequently applied way to solve problems for humans.

Page 8: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Case-based Problem Solving

It has been shown that human uses past-cases as models when learning to solve problems in early learning.

Page 9: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Learning in CBR

CBR denotes a machine learning paradigm that enables

sustained and incremental learning by updating the case-base after a problem has been solved.

Page 10: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Learning in CBR

Effective learning in CBR requires a well worked methods in order to:

1. Extract relevant knowledge from the experience.

2. Integrate a case into an existing knowledge structure.

3. Index the case for later matching with a similar case.

Page 11: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Fundamentals of case-based reasoning methods

Central tasks to all CBR methods:

1. Identify the current problem situation.

2. Find a past case similar to the new one.

3. Suggest a solution to the current problem.

4. Evaluate the proposed solution.

5. Update the system by learning from this experience.

Page 12: [K Engine] Intorduction To Machine Planning, A Case Based Approach

CBR Cycle - Retrieve

1.

Retrieve the most similar case or cases.

Page 13: [K Engine] Intorduction To Machine Planning, A Case Based Approach

CBR Cycle - Reuse

2. Reuse the information and knowledge in that case to solve the problem.

Page 14: [K Engine] Intorduction To Machine Planning, A Case Based Approach

CBR Cycle - Revise

3. Revise the proposed solution.

Page 15: [K Engine] Intorduction To Machine Planning, A Case Based Approach

CBR Cycle - Retain

4. Retain the parts of this experience likely to be useful for future problem solving.

Page 16: [K Engine] Intorduction To Machine Planning, A Case Based Approach

CBR Cycle- Retrieve under the hood

1. Retrieve Identify Feature Search Initially Match Select

Page 17: [K Engine] Intorduction To Machine Planning, A Case Based Approach

CBR Cycle – Reuse under the hood

2. Reuse Copy or Adapt Transformational VS Derivational

Reuse.

Page 18: [K Engine] Intorduction To Machine Planning, A Case Based Approach

CBR Cycle – Revise under the hood

3. Revise Evaluate Solution. Repair Fault.

Page 19: [K Engine] Intorduction To Machine Planning, A Case Based Approach

CBR Cycle – Retain under the hood

4. Retain Extract Index Integrate

Page 20: [K Engine] Intorduction To Machine Planning, A Case Based Approach

CBR Tasks Decomposition

Page 21: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Problems in Case Representation

1. Deciding what to store in a case?

2. Finding an appropriate structure for describing the case content.

3. Deciding how the case memory (case-base) should be organized and indexed for fast retrieval and reuse?

4. How to integrate the case memory into a model of general domain knowledge.

Page 22: [K Engine] Intorduction To Machine Planning, A Case Based Approach

What are CBR Challenges?

Core problems address by CBR research can be grouped into 5 areas, a solution to them constitute a CBR method:1. Knowledge/Case representation.2. Retrieval methods.3. Reuse methods.4. Revise methods.5. Retain methods.

Page 23: [K Engine] Intorduction To Machine Planning, A Case Based Approach

What are CBR Challenges?

The BIG challenge is to come up with methods that are suited for problem solving and learning in a subject domain (e.g Medical), and for a particular application environments (e.g Heart failure diagnosis).

Page 24: [K Engine] Intorduction To Machine Planning, A Case Based Approach

When an architect starts a new design.

When a surgeon approaches an operation.

When you get to your car to go home.

We use CBP everyday.

Page 25: [K Engine] Intorduction To Machine Planning, A Case Based Approach

What is Case Based Planning?

CBP is the idea of planning as

remembering.Remembering success so they can be

reused.Remembering failure so they can be

avoided.Remembering repairs so they can be

applied.

Page 26: [K Engine] Intorduction To Machine Planning, A Case Based Approach

CBP view to planning

The theory of case-based planning sees planning as an activity that tests a planner`s understanding of the world.

Planning errors lead the planner to learn more about its causes, which gives it a better understanding of how to avoid them.

Page 27: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Planning theories basic features

Case-based planner searches its memory for plans that satisfy many of its goals at once.

Greedy

Page 28: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Planning theories basic features

Case-based planner treats errors as opportunities to learn more about its domain, and the problems that arise in it.

Positive thinker

Page 29: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Planning theories basic features

Case-based planner saves previous built plan in memory for later use in similar circumstances.

Skimpy

Page 30: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Building Initial Plan

Planner stores and organizes the past planning experience under two sort of indices:

1. Goals to be satisfied

2. Failures to be avoided.

Why ?

Page 31: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Building Initial Plan

The case-based approach to finding an initial plan is to anticipate problems so the planner can find plans that avoid them

Page 32: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Debugging Failed Plan (F5)

Expectation failure VS Planning failure.

How the planner responds to each of them? Planning failure -> Replanning. Expectation failure -> Repair faulty

knowledge.

Page 33: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Learning from planning

A case-based planner learns 31. Plan learning: learn new plans that

avoid problems.

2. Expectation learning: learn features that predict the problems.

3. Critic learning: learn the repairs that have to be made if those problems arise again in different circumstances

Page 34: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Why case-based?

We want a planner that can learn

and recall complex plans rather than having to repeat work it has already done.

Rule based VS Case based needs.

Page 35: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Retriever

To retrieve plans from memory, the plan Retriever needs

1. A memory of plans indexed by the goals they satisfy.

2. A goal similarity metric.

3. A value hierarchy of goals.

Page 36: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Modifier

To alter old plans to meet new goals, Modifier needs

1. A set of modification rules.

2. Critics.

3. Knowledge of goal specific requirements and general plan specification.

Page 37: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Storer

To place new plans in memory, Storer needs

. Index them under the same features that the Retriever uses to find them, the goals they satisfy and

the situation in which they are appropriate.

Page 38: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Repairer

To repair failed plans and describe them to Storer, the Repairer Requires

1. A vocabulary of plan failures.

2. A set of repair strategies that are indexed by that vocabulary.

Page 39: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Storer revisited

To place new plans in memory, Storer needs

. Index them under the same features that the Retriever uses to find them

1. The goals they satisfy.

2. The situation in which they are appropriate.

3. The problems they avoid

Page 40: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Assigner

To decide which features in a situation are to blame for a failure, the Assigner needs

Be able to describe the cause of the failure. The more extensive its vocab the more exact its credit assignment.

Page 41: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Anticipator

Anticipator take the features of the situation that the planner is dealing with, and recall problems that have arisen in past situations to handle them.

Page 42: [K Engine] Intorduction To Machine Planning, A Case Based Approach

Thanks!

Muhamad [email protected]


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